Neuroimaging of Visceral Pain

Ruffle, James K.; Frøkjær, Jens Brøndum; Farmer, Adam D.

doi:10.1007/978-3-319-48046-6_13

Cited by 2 publications

(5 citation statements)

References 140 publications

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“…[61][62][63] Visceral pain is often regarded as more unpleasant, and it is more difficult to localize than somatic pain. 64 Visceral hypersensitivity consists of 2 components: allodynia, in which an innocuous stimulus is perceived as painful, and hyperalgesia, defined as a more intense perception of a painful stimulus. 22 Patients with visceral hypersensitivity have more severe IBS symptoms.…”

Section: Visceral Sensationmentioning

confidence: 99%

“…70 Assessment of cortical activation or regional changes in cerebral blood flow with functional magnetic resonance imaging or positron emission tomography, respectively, provides insights into the central processing of visceral pain. 64 These studies assessed cortical activation at rest and during balloon distention, cortical thickness, and functional connectivity. The spinal and vagal afferents from the GI tract indirectly project to the thalamus, insula, amygdala, prefrontal cortex, primary somatosensory cortex, secondary somatosensory cortex, and cingulate cortices, including the anterior cingulate cortex.…”

Section: Visceral Sensationmentioning

confidence: 99%

“…The spinal and vagal afferents from the GI tract indirectly project to the thalamus, insula, amygdala, prefrontal cortex, primary somatosensory cortex, secondary somatosensory cortex, and cingulate cortices, including the anterior cingulate cortex. 64 The somatosensory cortices (the lateral pain system) serve to encode the intensity and location of visceral pain. The cingulate cortex (the medial pain system) is implicated in the emotional interpretation of the stimulusdspecifically, the affectivemotivational aspect (pain unpleasantness and related anxiety) and the cognitiveevaluative aspect (anticipation and attention of pain).…”

Section: Visceral Sensationmentioning

confidence: 99%

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Sex- and Gender-Related Differences in Common Functional Gastroenterologic Disorders

Narayanan

Anderson²,

Bharucha

2021

Mayo Clinic Proceedings

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Functional gastrointestinal (GI) disorders (FGIDs) result from central and peripheral mechanisms, cause chronic remitting-relapsing symptoms, and are associated with comorbid conditions and impaired quality of life. This article reviews sex-and gender-based differences in the prevalence, pathophysiologic factors, clinical characteristics, and management of functional dyspepsia (FD) and irritable bowel syndrome (IBS) that together affect approximately 1 in 4 people in the United States. These conditions are more common in women. Among patients with IBS, women are more likely to have severe symptoms and coexistent anxiety or depression; constipation or bloating and diarrhea are more common in women and men, respectively, perhaps partly because defecatory disorders, which cause constipation, are more common in women. Current concepts suggest that biological

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Section: Visceral Sensationmentioning

confidence: 99%

Section: Visceral Sensationmentioning

confidence: 99%

Section: Visceral Sensationmentioning

confidence: 99%

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Sex- and Gender-Related Differences in Common Functional Gastroenterologic Disorders

Narayanan

Anderson²,

Bharucha

2021

Mayo Clinic Proceedings

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“…), they remain relatively scarce in comparison to those evaluating other aspects of GI sensation, such as visceral pain (Ruffle et al . ). In previous studies utilising functional magnetic resonance imaging (fMRI), researchers variably demonstrated nausea‐related activation of a number of brain areas salient to interoception such as the amygdala, putamen, pons, locus coeruleus, and others associated with fear conditioning, such as the anterior insula and middle cingulate (Napadow et al .…”

Section: Introductionmentioning

confidence: 97%

“…Current understanding of the CNS changes that accompany the development and severity of nausea that individuals experience remains limited. Although functional neuroimaging techniques have been applied to the study of nausea (Napadow et al 2013b;Farmer et al 2015;Toschi et al 2017), they remain relatively scarce in comparison to those evaluating other aspects of GI sensation, such as visceral pain (Ruffle et al 2017). In previous studies utilising functional magnetic resonance imaging (fMRI), researchers variably demonstrated nausea-related activation of a number of brain areas salient to interoception such as the amygdala, putamen, pons, locus coeruleus, and others associated with fear conditioning, such as the anterior insula and middle cingulate (Napadow et al 2013b;Farmer et al 2015;Sclocco et al 2016;Toschi et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Functional brain networks and neuroanatomy underpinning nausea severity can predict nausea susceptibility using machine learning

Ruffle

Patel

Giampietro

et al. 2019

The Journal of Physiology

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Key points Nausea is an adverse experience characterised by alterations in autonomic and cerebral function. Susceptibility to nausea is difficult to predict, but machine learning has yet to be applied to this field of study. The severity of nausea that individuals experience is related to the underlying morphology (shape) of the subcortex, namely of the amygdala, caudate and putamen; a functional brain network related to nausea severity was identified, which included the thalamus, cingulate cortices (anterior, mid‐ and posterior), caudate nucleus and nucleus accumbens. Sympathetic nervous system function and sympathovagal balance, by heart rate variability, was closely related to both this nausea‐associated anatomical variation and the functional connectivity network, and machine learning accurately predicted susceptibility or resistance to nausea. These novel anatomical and functional brain biomarkers for nausea severity may permit objective identification of individuals susceptible to nausea, using artificial intelligence/machine learning; brain data may be useful to identify individuals more susceptible to nausea. Abstract Nausea is a highly individual and variable experience. The central processing of nausea remains poorly understood, although numerous influential factors have been proposed, including brain structure and function, as well as autonomic nervous system (ANS) activity. We investigated the role of these factors in nausea severity and if susceptibility to nausea could be predicted using machine learning. Twenty‐eight healthy participants (15 males; mean age 24 years) underwent quantification of resting sympathetic and parasympathetic nervous system activity by heart rate variability. All were exposed to a 10‐min motion‐sickness video during fMRI. Neuroanatomical shape differences of the subcortex and functional brain networks associated with the severity of nausea were investigated. A machine learning neural network was trained to predict nausea susceptibility, or resistance, using resting ANS data and detected brain features. Increasing nausea scores positively correlated with shape variation of the left amygdala, right caudate and bilateral putamen (corrected P = 0.05). A functional brain network linked to increasing nausea severity was identified implicating the thalamus, anterior, middle and posterior cingulate cortices, caudate nucleus and nucleus accumbens (corrected P = 0.043). Both neuroanatomical differences and the functional nausea‐brain network were closely related to sympathetic nervous system activity. Using these data, a machine learning model predicted susceptibility to nausea with an overall accuracy of 82.1%. Nausea severity relates to underlying subcortical morphology and a functional brain network; both measures are potential biomarkers in trials of anti‐nausea therapies. The use of machine learning should be further investigated as an objective means to develop models predicting nausea susceptibility.

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Neuroimaging of Visceral Pain

Cited by 2 publications

References 140 publications

Sex- and Gender-Related Differences in Common Functional Gastroenterologic Disorders

Sex- and Gender-Related Differences in Common Functional Gastroenterologic Disorders

Functional brain networks and neuroanatomy underpinning nausea severity can predict nausea susceptibility using machine learning

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